implementing NB classifier

PppBr · PppBr · commit 14c0a45e6f2f · 2019-03-28T15:44:07.000-05:00
diff --git a/TensorFlow.NET.sln b/TensorFlow.NET.sln
@@ -11,6 +11,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\T
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Visualization", "src\TensorFlowNET.Visualization\TensorFlowNET.Visualization.csproj", "{0254BFF9-453C-4FE0-9609-3644559A79CE}"
 EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{A4428462-1147-45E9-8403-4E6860FF6EED}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -33,6 +35,10 @@ Global
 		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Release|Any CPU.Build.0 = Release|Any CPU
+		{A4428462-1147-45E9-8403-4E6860FF6EED}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{A4428462-1147-45E9-8403-4E6860FF6EED}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{A4428462-1147-45E9-8403-4E6860FF6EED}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{A4428462-1147-45E9-8403-4E6860FF6EED}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE
diff --git a/src/TensorFlowNET.Core/APIs/tf.math.cs b/src/TensorFlowNET.Core/APIs/tf.math.cs
@@ -152,6 +152,9 @@ public static Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip
         public static Tensor sub(Tensor a, Tensor b) 
             => gen_math_ops.sub(a, b);
 
+        public static Tensor divide(Tensor a, Tensor b)
+            => gen_math_ops.real_div(a, b);
+
         public static Tensor sqrt(Tensor a, string name = null) 
             => gen_math_ops.sqrt(a, name);
 
diff --git a/src/TensorFlowNET.Core/Operations/Distributions/distribution.py.cs b/src/TensorFlowNET.Core/Operations/Distributions/distribution.py.cs
@@ -43,7 +43,7 @@ public Tensor log_prob(Tensor value, string name = "log_prob")
 
         private Tensor _call_log_prob (Tensor value, string name)
         {
-            with(ops.name_scope(name, "moments", new { value }), scope =>
+            return with(ops.name_scope(name, "moments", new { value }), scope =>
             {
                 try
                 {
@@ -60,10 +60,9 @@ private Tensor _call_log_prob (Tensor value, string name)
                     }
                 }
             });
-            return null;
         }
 
-        private Tensor _log_prob(Tensor value)
+        protected virtual Tensor _log_prob(Tensor value)
         {
             throw new NotImplementedException();
         }
diff --git a/src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs b/src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs
@@ -79,25 +79,32 @@ public Tensor _batch_shape()
             return array_ops.broadcast_static_shape(new Tensor(_loc.shape), new Tensor(_scale.shape));
         }
 
-        private Tensor _log_prob(Tensor x)
+        protected override Tensor _log_prob(Tensor x)
         {
-            return _log_unnormalized_prob(_z(x)) -_log_normalization();
+            var log_prob = _log_unnormalized_prob(_z(x));
+            var log_norm = _log_normalization();
+            return log_prob - log_norm;
         }
 
         private Tensor _log_unnormalized_prob (Tensor x)
         {
             return -0.5 * math_ops.square(_z(x));
         }
-
+        /// <summary>
+        /// Standardize input `x` to a unit normal.
+        /// </summary>
+        /// <param name="x"></param>
+        /// <returns></returns>
         private Tensor _z (Tensor x)
         {
-            return (x - this._loc) / this._scale;
+            return tf.divide(tf.sub(x, this._loc), this._scale);
         }
 
         private Tensor _log_normalization()
         {
-            Tensor t = new Tensor(Math.Log(2.0 * Math.PI));
-            return 0.5 * t + math_ops.log(scale());
+            Tensor t1 = ops.convert_to_tensor(Math.Log(2.0 * Math.PI), TF_DataType.TF_FLOAT);
+            Tensor t2 = tf.multiply(ops.convert_to_tensor(0.5, TF_DataType.TF_FLOAT), t1);
+            return  tf.add(t2, math_ops.log(this._scale));
         }
     }
 }
diff --git a/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj b/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
@@ -58,4 +58,8 @@ Bug memory leak issue when allocating Tensor.</PackageReleaseNotes>
     <Folder Include="Keras\Initializers\" />
   </ItemGroup>
 
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
+  </ItemGroup>
+
 </Project>
diff --git a/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs b/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs
diff --git a/test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

Original file line number	Diff line number	Diff line change
`@@ -43,7 +43,7 @@ public Tensor log_prob(Tensor value, string name = "log_prob")`
`43`	`43`
`44`	`44`	`private Tensor _call_log_prob (Tensor value, string name)`
`45`	`45`	`{`
`46`		`- with(ops.name_scope(name, "moments", new { value }), scope =>`
	`46`	`+ return with(ops.name_scope(name, "moments", new { value }), scope =>`
`47`	`47`	`{`
`48`	`48`	`try`
`49`	`49`	`{`
`@@ -60,10 +60,9 @@ private Tensor _call_log_prob (Tensor value, string name)`
`60`	`60`	`}`
`61`	`61`	`}`
`62`	`62`	`});`
`63`		`- return null;`
`64`	`63`	`}`
`65`	`64`
`66`		`- private Tensor _log_prob(Tensor value)`
	`65`	`+ protected virtual Tensor _log_prob(Tensor value)`
`67`	`66`	`{`
`68`	`67`	`throw new NotImplementedException();`
`69`	`68`	`}`
Original file line number	Diff line number	Diff line change
`@@ -79,25 +79,32 @@ public Tensor _batch_shape()`
`79`	`79`	`return array_ops.broadcast_static_shape(new Tensor(_loc.shape), new Tensor(_scale.shape));`
`80`	`80`	`}`
`81`	`81`
`82`		`- private Tensor _log_prob(Tensor x)`
	`82`	`+ protected override Tensor _log_prob(Tensor x)`
`83`	`83`	`{`
`84`		`- return _log_unnormalized_prob(_z(x)) -_log_normalization();`
	`84`	`+ var log_prob = _log_unnormalized_prob(_z(x));`
	`85`	`+ var log_norm = _log_normalization();`
	`86`	`+ return log_prob - log_norm;`
`85`	`87`	`}`
`86`	`88`
`87`	`89`	`private Tensor _log_unnormalized_prob (Tensor x)`
`88`	`90`	`{`
`89`	`91`	`return -0.5 * math_ops.square(_z(x));`
`90`	`92`	`}`
`91`		`-`
	`93`	`+ /// <summary>`
	`94`	+ /// Standardize input `x` to a unit normal.
	`95`	`+ /// </summary>`
	`96`	`+ /// <param name="x"></param>`
	`97`	`+ /// <returns></returns>`
`92`	`98`	`private Tensor _z (Tensor x)`
`93`	`99`	`{`
`94`		`- return (x - this._loc) / this._scale;`
	`100`	`+ return tf.divide(tf.sub(x, this._loc), this._scale);`
`95`	`101`	`}`
`96`	`102`
`97`	`103`	`private Tensor _log_normalization()`
`98`	`104`	`{`
`99`		`- Tensor t = new Tensor(Math.Log(2.0 * Math.PI));`
`100`		`- return 0.5 * t + math_ops.log(scale());`
	`105`	`+ Tensor t1 = ops.convert_to_tensor(Math.Log(2.0 * Math.PI), TF_DataType.TF_FLOAT);`
	`106`	`+ Tensor t2 = tf.multiply(ops.convert_to_tensor(0.5, TF_DataType.TF_FLOAT), t1);`
	`107`	`+ return tf.add(t2, math_ops.log(this._scale));`
`101`	`108`	`}`
`102`	`109`	`}`
`103`	`110`	`}`